I-Corps: Customer discovery for breast cancer detection technology

I-Corps项目更广泛的影响/商业潜力是开发软件来创建和推荐个性化的乳房筛查计划，仅使用患者的第一次（基线）乳房x光检查。这种个体化护理可能会限制患者的辐射暴露，或为患者提供补充筛查。改善年轻女性的筛查程序可以提供更好的预后机会。此外，早期发现可以为针对易患癌症的乳腺组织提供新的治疗选择，从而有可能预防这种疾病。该软件将与乳腺癌风险的基因检测相媲美并互为补充。I-Corps项目的基础是开发一种拟议的计算机辅助检测软件，以及它在区分健康的、有组织的、致密的乳房组织与不健康的、无组织的、致密的乳房组织（通常与肿瘤发病有关）方面的潜力。健康组织和不健康组织之间的细微差别很难从视觉上识别出来。这项技术区分健康组织和不健康组织，以确定乳房x光检查中特别“危险”的乳腺组织子区域。这项技术可以解释乳房密度对乳腺癌风险增加的影响。计算机辅助乳腺癌检测软件已经使用了二十多年，但没有显著的临床改善。现有的计算机辅助检测程序因产生大量假阳性而受到医疗专业人士的批评，导致不必要的活组织检查和增加评估时间。拟议中的技术可能会解决这些问题，为患者及其家属提供一个压力较小的选择。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of software to create and recommend personalized breast screening plans, using only the patient’s first (baseline) mammogram. This individualized care could result in limiting the patient’s exposure to radiation or could provide the patient with access to supplemental screening. Improving the screening process for women at a young age offers the chance of a better prognosis. In addition, early detection could allow for novel treatment options to target cancer-prone breast tissue, potentially leading to the prevention of the disease. The software would be comparable and complementary to genetic testing for breast cancer risks.This I-Corps project is based on the development of a proposed computer-aided detection software and its potential to discriminate between healthy, organized, dense mammographic breast tissue versus unhealthy, disorganized, dense breast tissue often associated with the onset of a tumor. The subtle differences between healthy and unhealthy tissue are challenging to identify visually. This technology discriminates between healthy and unhealthy tissue to identify the sub-regions of mammographic breast tissue that are particularly “risky”. The technology may explain the effect breast density has on someone's increased risk for breast cancer. Computer-aided breast cancer detection software has been used for over two decades without signficant clinical improvement. The existing computer-aided detection programs have been criticized by medical professionals for producing high numbers of false positives, resulting in unnecessary biopsies and increased evaluation times. The proposed technology may solve these problems, offering a less stressful option to patients and their families.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.